An algorithm for identification of natural disaster affected area

An important source of information presently is social media, which reports any major event including natural disasters. Social media also includes conversational data. As a result, the volume of data on social media has an enormous increase. During the time of natural disaster like floods, tsunami, earthquake, landslide, etc., people require information in those situations, so that relief operations like help, medical facilities can save many lives (Bifet et al. in J Mach Learn Res Proc Track 17:5–11, 2011 ). An attempt is made in this article on Geoparsing which will identify the places of disaster on a Map. Geoparsing is a process of converting free text description of locations into the geographical identifier in an unambiguous manner with the help of longitude and latitude. With the help of geographical coordinates, it can be mapped and entered into geographical information system. A real-time, reliable at robust twitter messages which are the source of the information can handle a large amount of data. After collecting tweets at the real time we can parse them for the disaster situation and its location. This information will help to identify the exact location of the event. For knowing information on the natural disaster, tweets are extracted from twitter to R-Studio environment. First the extracted tweets from twitter are parsed using R about “Natural Disaster”. Later we parsed the tweets and store in CSV format in R database. For all posted data tweets are calculated and stored in a file. Later visual analysis is performed for the data store using R Statistical Software. Further, it is useful to assess the severity of the natural disaster. Sentiment analysis (Rahmath in IJAIEM 3(5):1–3, 2014 ) of user tweets is useful for decision making (Rao et al. in Int J Comput Sci Inf Technol 6(3):2923–7, 2015 ).